Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Support vector machines are universally consistent
Journal of Complexity
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Maximum-Gain Working Set Selection for SVMs
The Journal of Machine Learning Research
Solving multiclass support vector machines with LaRank
Proceedings of the 24th international conference on Machine learning
Second-order smo improves svm online and active learning
Neural Computation
The Journal of Machine Learning Research
Sequence Labelling SVMs Trained in One Pass
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Nonconvex Online Support Vector Machines
IEEE Transactions on Pattern Analysis and Machine Intelligence
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LaRank is a multi-class support vector machine training algorithm for approximate online and batch learning based on sequential minimal optimization. For batch learning, LaRank performs one or more learning epochs over the training set. One epoch sequentially tests all currently excluded training examples for inclusion in the dual optimization problem, with intermittent reprocess optimization steps on examples currently included. Working set selection for one reprocess step chooses the most violating pair among variables corresponding to a random example. We propose a new working set selection scheme which exploits the gradient update necessarily following an optimization step. This makes it computationally more efficient. Among a set of candidate examples we pick the one yielding maximum gain between either of the classes being updated and a randomly chosen third class. Experiments demonstrate faster convergence on three of four benchmark datasets and no significant difference on the fourth.